European Integration, Productivity Growth and Real Convergence: Evidence from the New Member States Ali M. Kutan and Taner M. Yigit Kutan – Southern Illinois University Edwardsville; ZEI, Bonn; EMG, London; and WDI, Michigan. Yigit – Bilkent University, Ankara

Objective To analyze the determinants of labor productivity in 8 new members of the European Union that joined in 2004 CEE8 - the Czech Republic, Estonia, Hungary, Poland, Latvia, Lithuania, Slovakia and Slovenia

Focus On the impact of globalization and integration factors explaining productivity CEE8 provides a good case study Since 1990s significant opening up and integration towards the West have been taking place

Motivation: Why labor productivity? Growth in labor productivity raised income per capita in CEE8 countries more than that in employment and population (World Bank Report, 2008) CEE8 labor productivity has grown at a rate greater than many other emerging and developing countries, including Russia and Ukraine (Rada and Taylor, 2006).

Why labor productivity? The net cumulative productivity change during ranged from 109 % (Estonia) to 41 % ( Slovenia) The biggest productivity increases took place in the three Baltic States: Estonia (109 %), Lithuania (104 %) and Latvia (93 %) Hungary and Poland are the next biggest productivity gainers with a net cumulative gain of 75 and 68 %, respectively Net cumulative productivity gain in EU 15 during the same time period was only 24.2 %

Why labor productivity?

Significant volatility in labor productivity is a concern – what causes it? Implications for real income convergence and euro-area policy Income growth through productivity increases is important if excessive population movements from the new members to the old are to be avoided and if the EU's budget is not to be strained by transfers to lagging economies.

Theoretical Model We use the theoretical model developed in Bernard and Jones (RESTAT,1996a; AER, 1996b) and Cameron et al (EER, 2005) It is shown that productivity growth is the result of country specific innovation or technology transfers from frontier countries

Innovation Variables Theory suggest that both domestic and international factors boost innovation. Most commonly used variables in the literature are FDI, trade, R & D spending, and human capital We are particularly interested in international factors here as they are directly related to the integration efforts of CEE8 countries

Technology transfer Technology transfer is defined as the distance in productivity level between non- frontier countries and the frontier country Employed as a proxy for technology transfer and convergence in technical efficiency Trade data is typically used to control for the rate of technological transfer

Empirical Specification

Dependent variable: Labor productivity growth Zt vector includes globalization variables (exports, imports, and FDI,) and domestic innovation variables (human capital, and R & D expenditures). Domestic investment is also included as a control variable Distance variable, a proxy for the rate of technology transfer from the frontier, is measured by the absolute value of the log ratio of productivity of country i to the productivity of EU15.

Expected signs Innovation variables are expected to have all positive, but recent studies show that imports may have a negative influence (Kasahara and Rodrique, JDE, 2008 and Blalock and Veloso, WD, 2007). Imports may have a positive or negative sign depending upon (i) the composition of imports (i.e., high-tech vs consumption goods), (ii) the relative cost of imports with respect to local substitutes, (iii) varieties, and (iv) the relative usage of imported intermediaries in production with respect to domestic ones

Expected signs Distance variable is measured by the absolute value of the log ratio of productivity of country i to the productivity of EU15. As the productivity gap between country i and the frontier EU15 widens, this ratio grows smaller, making the absolute value of the log a larger number. With larger numbers, or a larger gap, the productivity growth of country i is expected to be faster, indicating a positive coefficient.

Methodology and Sample Period The estimations are carried out using a fixed effects panel estimation correcting for potential heteroskedasticity in the cross sectional dimension Sample period: Annual data

Dependent and Explanatory Variables Dependent variable: change in labor productivity of industry obtained from the Transition Reports of the EBRD FDI, exports, imports, domestic investment, and education (expressed as a ratio of GDP, except education % of population) Distance (the absolute value of the log ratio of productivity of country i to the productivity of EU15) is computed using data real output per worker. Other data sources: AMECO of the European Commission's Directorate General for Economic and Financial Affairs and the EuroStat

Table 1: Descriptive Statistics: Mean (Variance) Prod. GrowthDistanceR&DEducationImportExportFDI CZE 3.7 (59.2)1.28 (0.01)1.2 (0.03)86.3 (11.5)62.6 (43.4)60.8 (78.0)5.7 (11.3) EST 9.0 (58.1)2.05 (0.06)0.6 (0.15)85.7 (14.3)74.2 (38.7)74.8 (25.8)8.0 (24.0) HUN 6.2 (12.7)1.60 (0.01)0.8 (0.02)69.8 (39.7)63.0 (116.4)22.2 (1.2)7.3 (12.9) LAT 7.7 (38.9)2.28 (0.04)0.4 (0.01)81.9 (8.8)54.2 (36.0)43.7 (7.1)4.5 (3.5) LIT 8.6 (47.9)2.43 (0.05)0.6 (0.01)83.9 (11.5)58.8 (33.2)50.4 (31.7)3.5 (3.8) POL 5.6 (12.6)1.71 (0.02)0.6 (0.00)79.6 (18.2)31.9 (36.6)29.1 (39.5)3.4 (1.1) SLA 4.0 (23.9)1.81 (0.01)0.7 (0.04)83.1 (23.1)73.3 (96.6)67.8 (107.4)4.5 (22.2) SLE 3.3 (32.8)0.74 (0.01)1.5 (0.01)74.9 (23.1)57.0 (23.8)55.7 (27.3)1.9 (3.5) EU (0.01)61.3 (15.5)31.6 (11.8)35.6 (11.7)2.3 (2.8)

Table 2: Productivity Growth Estimates (1)(2)(3)(4)(5)(6)(7) C **-53.11**-53.81**-51.44**-62.68**-61.81** (-3.18)(-3.03)(-3.15)(-2.8)(-3.11)(-2.91)(-3.36) Distance(-1) 9.69**9.15*10.38*9.98*9.57* (2.04)(1.86)(1.88)(1.76)(1.68)(1.64)(1.84) R&D(-1) (0.05)(-0.2)-0.08(0.06)(0.05)(0.06)(0.1) Education(-1) 0.73**0.76**0.73**0.70**0.96**0.95**0.83 (3.83)(3.74)(3.75)(3.39)(3.53)(3.38)(4.28) Imports(-1) -0.58**-0.57**-0.58**-0.57**-0.58** (-6.21)(-5.91)(-6.20)(-6.12)(-5.91)(-5.60)(-6.54) Exports 0.50**0.47**0.51** 0.40** 0.43 (3.55)(3.08)(3.30)(3.21)(2.39)(2.37)(2.93) FDI 0.35**0.38**0.34**0.33**0.40** 0.36 (2.73)(2.77)(2.58)(2.35)(2.66)(2.55)(2.86) Investment (1.34)(1.19)(1.20)(1.19)(0.39)(0.37)(0.79) D*RD11.06 (0.74) D*Education (0.26)(0.23)(0.08)(0.09) D*Imports (-0.36)(0.86)(0.80) D*Exports (-1.54)(-1.49)(-1.47) D*FDI-0.08 (-0.12) R2R N78

Empirical Findings Among the domestic factors, R&D expenditures do not have any impact on productivity changes, perhaps due to very small amounts (1-2 percent of GDP) However, secondary school enrollment ratio has a very significant effect on productivity changes (coefficient value:.73)

Empirical Findings Regarding international factors, exports and FDI variables have a positive and significant coefficients (0.50 and 0.35, respectively) However, imports has a negative and significant effect and its economic significance is large (the estimated coefficient is ) Overall, globalization has a net positive impact on productivity: A 1 percent increase in global activity (FDI and net trade flows) improves labor productivity by 0.27 ( ) percent.

Empirical Findings With respect to technological transfer, the distance coefficient is positive and statistically significant, indicating significant technology transfer from EU 15 facilitating “catching up” by CEE8 countries towards EU standards However, international and domestic variables used here do not seem to help facilitate technology transfer rate, perhaps due to a relatively small number of observations we have.

Further Research Areas Further evidence at micro level is necessary to better understand the imports-productivity link Almost no research on this issue for CEE8 countries Halpern et al. (2006) employ a panel data of Hungarian firms to test the impact of imports on productivity. They argue that imports may influence labor productivity through a quality and a variety channel and find empirical evidence supporting these channels

Further Research Areas Why variables typically used in the literature (FDI, trade, R & D) do not seem to facilitate technology transfer rate Data problems – small sample size? Unobserved or informal factors such as informal economy and its size? The estimated model explains only 41 percent of variation in labor productivity growth. Further research employing more formal and informal indicators of productivity would be fruitful.

Conclusions and Policy Implications Two channels of labor productivity growth (country-specific innovation and technology transfer) are at work As innovation variables, globalization (FDI and exports) and human capital have been the key sources of positive productivity growth in CEE8 during There is convergence of transfer technology, raising productivity growth which is essential for convergence to core European income levels Policymakers need to design further reforms to encourage exports and FDI growth

Conclusions and Policy Implications Policymakers need to better understand the forces driving the rate of technology transfer as variables typically used in the literature may not seem facilitate it entirely. For example, how important is the informal economy for productivity? How does it facilitate technology transfer? Aggregate impact of imports on labor productivity is negative. Although policymakers care about aggregate impacts, it is important to look at the disaggregated data to better understand the imports-productivity link to design appropriate policies to deal with the problem Overall, further research on the primary drivers of labor productivity growth at the aggregate, sectoral and firm level would be worthwhile.